code: def test_save_restore(self):
      dates = pd.date_range("1/1/2019", periods=8)
      values = np.random.randn(8)
      df = pd.DataFrame({"dt": dates, "v": values})
  
      future_seq_len = 2
      dt_col = "dt"
      target_col = "v"
      drop_missing = True
      feat = TimeSequenceFeatureTransformer(
          future_seq_len=future_seq_len,
          dt_col=dt_col,
          target_col=target_col,
          drop_missing=drop_missing,
      )
  
      feature_list = feat.get_feature_list()
      config = {
          "selected_features": json.dumps(feature_list),
          "past_seq_len": 2,
      }
  
      train_x, train_y = feat.fit_transform(df, **config)
  
      dirname = tempfile.mkdtemp(prefix="automl_test_feature")
      try:
          save(dirname, feature_transformers=feat)
          new_ft = TimeSequenceFeatureTransformer()
          restore(dirname, feature_transformers=new_ft, config=config)
  
          assert new_ft.future_seq_len == future_seq_len
          assert new_ft.dt_col == dt_col
          assert new_ft.target_col[0] == target_col
          assert new_ft.extra_features_col is None
          assert new_ft.drop_missing == drop_missing
  
          test_x, _ = new_ft.transform(
              df[:-future_seq_len], is_train=False
          )
  
          assert_array_almost_equal(test_x, train_x, decimal=2)
  
      finally:
          shutil.rmtree(dirname)
  
imports: import pytest
  from test.zoo.pipeline.utils.test_utils import ZooTestCase
  from zoo.chronos.autots.deprecated.feature.utils import save, restore
  from zoo.chronos.autots.deprecated.feature.time_sequence import *
  from numpy.testing import assert_array_almost_equal
  import json
  import tempfile
  import shutil
  from zoo.chronos.autots.deprecated.preprocessing.utils import train_val_test_split
label_desc: Use image node_id 13 for calling the TestTimeSequenceFeature obj's underlying member method code with example usage: obj.test_save_restore() without return types